Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network. Issue 9 (28th May 2018)

• Record Type: Journal Article
• Authors: Tong, Zhang; Lanxiang, Sun; Jianchang, Liu; Haibin, Yu; Xiaoming, Zhou; Lin, Gao; Yingwei, Zhang
• Is Part Of: Electric power components and systems. Volume 46:Issue 9(2018)
• Page Start: 987
• Page End: 998
• Publication Date: 2018-05-28
• Subjects: Active distribution network (ADN) -- fault location analysis -- high resistance fault -- phase measurement unit (PMU); Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2018.1460884
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: A fault diagnosis and location method of artificial neural network (ANN) based on regularized radial basis function (RRBF) is proposed. The phase angle feature of fault voltage and current signal is analyzed. The proposed method adopts synchronized amplitude and phase angle feature for fault diagnosis based on RRBF neural network. The fault diagnosis and location for the distribution branch is researched in the IEEE 13-bus active distribution network (ADN) system. The diagnosis accuracy and location precision is analyzed considering the effect of different input signals, fault position, and fault resistance. The simulation result demonstrates that the location method based on phase angle feature shows higher accuracy. The RRBF fault diagnosis and location method aims to solve fault in ADN and lays the foundation to maintain ADN system stability.